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NPTEL Deep Learning IIT Ropar Assignment 2 Answers 2022

We Discuss About That NPTEL Deep Learning IIT Ropar Assignment 2 Answers 2022

NPTEL Deep Learning IIT Ropar Assignment 2 Answers 2022 – Here All The Questions and Answers Provided to Help All The Students and NPTEL Candidate as a Reference Purpose, It is Mandetory to Submit Your Weekly Assignment By Your Own Understand Level.

Are you looking for the Assignment Answers to NPTEL Deep Learning IIT Ropar Assignment 2 Answers 2022? If Yes You are in Our Great Place to Getting Your Solution, This Post Should be help you with the Assignment answer to the National Programme on Technology Enhanced Learning (NPTEL) Course “NPTEL Deep Learning IIT Ropar Assignment 2 Answers 2022”

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NPTEL Deep Learning IIT Ropar Assignment

ABOUT THE COURSE :
Deep Learning has received a lot of attention over the past few years and has been employed successfully by companies like Google, Microsoft, IBM, Facebook, Twitter etc. to solve a wide range of problems in Computer Vision and Natural Language Processing. In this course we will learn about the building blocks used in these Deep Learning based solutions. Specifically, we will learn about feedforward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms. We will also look at various optimization algorithms such as Gradient Descent, Nesterov Accelerated Gradient Descent, Adam, AdaGrad and RMSProp which are used for training such deep neural networks. At the end of this course students would have knowledge of deep architectures used for solving various Vision and NLP tasks
INTENDED AUDIENCE:  Any interested learner
PREREQUISITES:  10 hrs of pre-course material will be provided, learners need to practise this to be ready to take the course.
INDUSTRY SUPPORT:  HONEYWELL, ABB, FORD, GYAN DATA PVT. LTD.

Next Week Assignment Answers

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This course can have Associate in Nursing unproctored programming communication conjointly excluding the Proctored communication, please check announcement section for date and time. The programming communication can have a weightage of twenty fifth towards the ultimate score.

Final score = Assignment score + Unproctored programming exam score + Proctored Exam score
  • Assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
  • ( All assignments in a particular week will be counted towards final scoring – quizzes and programming assignments). 
  • Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam – out of 100
  • Proctored Exam score =50% of the proctored certification exam score out of 100
YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF ASSIGNMENT SCORE >=10/25 AND
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50. 
If any one of the 3 criteria is not met, you will not be eligible for the certificate even if the Final score >= 40/100. 

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Q1. How many Boolean functions can be designed with 3 inputs?

Ans:- C

Q2. Pick out the function(s) that are not linearly separable?

Ans:- A, D

Q3. Out of the functions that can be designed from n inputs, how many of them are linearly separable?

Ans:-D

Q4. Which of the following statements are TRUE?

Statement I. The given network of perceptrons can be used to implement any complex boolean input functions.

Statement II. Each Wi can be adjusted to get desired output for that input.

Ans:-C

Q5. Consider you are given a Boolean function with 5 inputs. It is represented by a network of perceptrons containing one hidden layer and one output layer with one perceptron. How many perceptrons are there in the hidden layer?

Ans:-A

Q6. Assume you have a perceptron to solve a problem of deciding if a student is eligible for scholarship or not. We have only one input in this case. Bias being 50%. What will be the decision of the model when the student scored 0.49 and 0.51?

Ans:-C

Q7. I. Logistic function is smooth and continuous. II. Logistic function is differentiable.

Ans:-C

Q8. Select all that applies to a learning algorithm.

Ans:-A,C,D

Q9. Sum of squared error is better than sum of errors. Why is this true?

Ans:-B

Q10. Consider a machine learning model, with only one input x and output y. Given training instances, (x,y) = (0.4, 0.3), (1.8, 0.6), w = 1.2, b = -1.4 and the function is logistic sigmoid function. Compute the loss function, L(w,b)=12∑Ni=1(yif(xi))2

Ans:-0.0059


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